GPU-accelerated spatial omics analysis with SpaCET deconvolution and SecAct signaling.
# CPU (works on Windows, Mac, Linux)
docker run -it --rm -v $(pwd):/workspace psychemistz/spatial-gpu:latest
# GPU (requires NVIDIA Container Toolkit)
docker run -it --rm --gpus all -v $(pwd):/workspace psychemistz/spatial-gpu:gpu
# Jupyter Lab
docker run -it --rm -p 8888:8888 -v $(pwd):/workspace psychemistz/spatial-gpu:latest \
jupyter lab --ip=0.0.0.0 --no-browser --allow-root
| Tag | Python | GPU (CuPy) | R (SpaCET) | SecAct | Use Case |
|---|---|---|---|---|---|
latest, cpu | 3.x | - | - | Yes | Standard analysis |
gpu | 3.x | CUDA 12.2 | - | Yes | GPU-accelerated analysis |
with-r, cpu-with-r | 3.x | - | Yes | Yes | R cross-validation |
gpu-with-r | 3.x | CUDA 12.2 | Yes | Yes | Full stack |
# Start CPU container
docker compose up -d spatial-gpu
# Start GPU container
docker compose up -d spatial-gpu-gpu
# Jupyter Lab (CPU: port 8888, GPU: port 8889, R: port 8890)
docker compose up spatial-gpu-jupyter
docker compose up spatial-gpu-jupyter-gpu
# Enter running container
docker compose exec spatial-gpu bash
# CPU (default)
docker build -t spatial-gpu:latest .
# GPU
docker build -t spatial-gpu:gpu --build-arg USE_GPU=true .
# With R SpaCET
docker build -t spatial-gpu:with-r --build-arg INSTALL_R=true .
# GPU + R (full stack)
docker build -t spatial-gpu:gpu-with-r --build-arg USE_GPU=true --build-arg INSTALL_R=true .
| Platform | CPU | GPU |
|---|---|---|
| Linux (x86_64) | Yes | Yes (NVIDIA GPU + Container Toolkit) |
| macOS (Intel) | Yes | No |
| macOS (Apple Silicon) | Yes (Rosetta 2) | No |
| Windows (WSL2) | Yes | Yes (NVIDIA GPU + WSL2 CUDA) |
docker run --gpus all psychemistz/spatial-gpu:gpu nvidia-smiPython: spatial-gpu (spatialgpu), secactpy, scanpy, squidpy, anndata, scikit-learn, matplotlib, jupyter
GPU (gpu tag): CuPy (CUDA 12.2)
R (with-r tags): SpaCET, Seurat, Matrix, Rcpp, RcppArmadillo, rhdf5
Content type
Image
Digest
sha256:97d3c0f1b…
Size
843.7 MB
Last updated
6 months ago
docker pull psychemistz/spatial-gpu